Automatically (e.g., by a computer) distinguishing between humans from computers in a networked computing environment, such as the Internet, is a difficult task. Many automated computational tasks are performed by computer processes (e.g., “bots”) that automatically crawl websites to obtain content. Such bots are growing in sophistication and are able to provide inputs to websites and other online resources. Bots may increase traffic in online networks, reducing available bandwidth for actual human users. Furthermore, bots may increasingly be used for malicious activities, such as denial of service attacks.
Another way in which bots have been used to exploit computer networks is through crowd-sourced tasks. In the context of natural language understanding (“NLU”), crowds can generate creative input for open ended questions, which then can be used as bootstrapping data for NLU models. It is difficult, however, to prevent the use of bots that act as human responders in order to “game” the system.
Conventional Completely Automated Public Turing test to tell Computers and Humans Apart (“CAPTCHA”)-style challenges have been developed in an attempt to distinguish humans from computers. Conventional CAPTCHA challenges provide an image of characters, typically skewed to make it difficult for bots to perform image recognition, but usually easy for the human brain to decipher. However, such challenges can be prone to image recognition techniques and also cannot be used to further screen respondents based on whether they know the answer to a challenge (i.e., to select only those respondents with a basic knowledge of particular subject matter). These and other drawbacks exist with conventional CAPTCHA challenges.